The data used in this section of the assignment is from the United Nations, 2022, and can be found at World population prospects - population division. It contains Population, Fertility, Mortality and Migration estimates for 237 countries and/or regions from 1950 to 2021. Only the following selected variables listed in Table 1.1 have been used in this section of the assignment.
Note: the original data set contained population values in January and July. Only January data has been chosen the same analysis could be completed with July data but was not done in this assignment.
# select only relatvent columns and rows
pop1<-select(population,c(3,9,11,12) ) %>%
filter(Type %in% c("World","Region"))
# change column types to numerical
pop1$Year<-as.numeric(pop1$Year)
pop1$`Total Population, as of 1 January (thousands)`<-as.numeric(pop1$`Total Population, as of 1 January (thousands)`)# create list of variables
names_data<-(names(pop1))
# create list of data types
data_type = c("Categorical","Categorical","Numerical","Numerical")
# create list of detailed information
further_information = c("Contains the following categories: World, Africa, Asia, Europe, Latin America and the Caribbean, Northern America, and Oceania.", "Contains the following information: World and Region","Contains years from 1950 to 2021", "Contains the population number in Thousands as of Janurary")
#join data sets together
names_data_final<-cbind(names_data,data_type,further_information)
# table of variable information, including column names and caption for table
kable(names_data_final,caption = "Variable Information.", col.names = c('Names','Data Type','Further Information')) %>%
kable_styling()| Names | Data Type | Further Information |
|---|---|---|
| Region, subregion, country or area * | Categorical | Contains the following categories: World, Africa, Asia, Europe, Latin America and the Caribbean, Northern America, and Oceania. |
| Type | Categorical | Contains the following information: World and Region |
| Year | Numerical | Contains years from 1950 to 2021 |
| Total Population, as of 1 January (thousands) | Numerical | Contains the population number in Thousands as of Janurary |
This filtered data set contains 4 variables. An outline of the variable types and information is presented above in Table 1.1. There are 504 observations in this filtered data set.
Table 1.2 shows the January population (in thousands) for the World and the Regions in 1950 and 2021. It than presents the Relative Population Change between these two years as a percentage.
# creating data set including relative difference
relative_diff<-filter(pop1,Year %in% c(1950,2021)) %>%
pivot_wider(names_from = Year,values_from = 'Total Population, as of 1 January (thousands)') %>%
mutate('Relative Difference (%)'= ((`2021`-`1950`)/`1950`)*100)
# creating table of relative difference data
kable(relative_diff,caption = "Relative Population Change of the World and Region poulations from 1950 to 2021.", digits = 0) %>%
kable_styling()| Region, subregion, country or area * | Type | 1950 | 2021 | Relative Difference (%) |
|---|---|---|---|---|
| WORLD | World | 2477675 | 7876932 | 218 |
| AFRICA | Region | 225120 | 1377285 | 512 |
| ASIA | Region | 1365953 | 4680790 | 243 |
| EUROPE | Region | 547304 | 745853 | 36 |
| LATIN AMERICA AND THE CARIBBEAN | Region | 166137 | 654148 | 294 |
| NORTHERN AMERICA | Region | 160754 | 374641 | 133 |
| OCEANIA | Region | 12406 | 44215 | 256 |
In Table 1.2 it is observed that the Worlds Relative Population Change between 1950 and 2021 is 218%, which means the population has more than doubled in the last 70 years. The table also indicates that the region with the greatest population change is Africa, this relative change has seen an increase of 512%. Meanwhile the region with the lowest Relative Population Change is Europe with an increase of only 36%. The reason for these changes was not explored but understanding these gains in population help to ensure appropriate resourcing occurs in these regions now and into the future.
Figure 1.1 shows the World population from 1950 to 2021. In blue is the population values at each year, while in orange is a linear regression line for this data.
# filter for world data only
world<-filter(pop1, Type == 'World')
# change column names to make graphing easier
colnames(world)[4] = "Population"
# graph a scatter graph with regression line
p<-ggplot(world,aes(x=Year, y=Population))+geom_point(color="#2375b3")+
# add regression line
geom_smooth(method = 'lm', se = FALSE,color = "#b36123")+
# show axis line clearly
theme(axis.line = element_line(linetype = 'solid'))+
# change y-axis label
labs(y="World Population Values in January (measured in thousands)")+
# change y scale numbers from scientific to numeric
scale_y_continuous(labels = label_number())
# add hover over and ensure graph is displayed
pp <- ggplotly(p)
ppFigure 1.1: The World Population in January (measured in thousands) from 1950 to 2021.
From Figure 1.1 it is observed that the World Population has steadily increased over time from 1950 to 2021. This increase shows a linear trend, with the population values being slightly above the regression line in the 1950s and from 2010 on wards, indicating the rate of increase in the population was slightly faster than a linear increase during these decades.
Figure 1.2 shows the population numbers in thousands in the regions from January 1950 to January 2021. All regions have the population values shown and a linear regression line (in black) for their population data.
# get world data only
region<-filter(pop1, Type == 'Region')
# change column names
colnames(region)[4] = "Population"
colnames(region)[1] = "Region"
# graph a scatter graph including regression line
p<-ggplot(region,aes(x=Year, y=Population, color= Region))+geom_line(size=1.5)+
# add linear regression line
geom_smooth(method = 'lm', se = FALSE, colour = '#353935',size =0.5)+
# show axis line clearly
theme(axis.line = element_line(linetype = 'solid'))+
# change y-axis label
labs(y="Region Population Values in January (measured in thousands)")+
# change y scale numbers from scientific to numeric
scale_y_continuous(labels = label_number())+
# graph each region on a seperate graph, ensure y-scale is free
facet_wrap(~Region, scales ="free",ncol=2)
# add hover over for further information and ensure graph is displayed
pp<-ggplotly(p) %>%
# change position of legend
layout(legend=list(orientation='h'))
ppFigure 1.2: The Region Populations in January (measured in thousands) from 1950 to 2021.
Figure 1.2 graphs the regions on separate axes due to the large variation in population sizes (these sizes can be seen in Table 1.2 for comparison). Note: the y-axis has different scales as a result of this.
An increase in population across all regions is observed in Figure 1.2, however the following further observations are also seen:
All of the above observations are made by visually comparing the individual regions to their linear regression lines further analysis of the type of regression was not conducted in this assignment to confirm the exact type of regression. From the above observations it can be concluded that steady linear growth has occurred in Asia; Latin America and the Caribbean; and North America in the past 70 years. Meanwhile, there has been a decline in the rate of population growth in Europe during the same time period. Finally, in Africa and Oceania the rate of growth has been faster with it appearing the exponential and/or polynomial.
United Nations. (2022). World population prospects - population division. https://population.un.org/wpp/